We propose a new computational framework for combinatorial problems arising in machine learning and computer vision. This framework is a special case of Lagrangean (dual) decomposition, but allows for efficient dual ascent (message passing) optimization. In a sense, one can understand both the framework and the optimization technique as a generalization of those for standard undirected graphical models (conditional random fields). We will make an overview of our recent results and plans for the nearest future.

Biography: Bogdan Savchynskyy is a senior researcher in TU Dresden. His main research interests are optimization problems in computer vision and machine learning. In particular, he is an author of a number of papers on exact and approximate inference for discrete graphical models. One of his recent works in this field has got an award at CVPR 14 conference.

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems